Eyes Wide Open - Optimising Cosmological Surveys in a Crowded Market
Abstract
Optimising the major next-generation cosmological surveys (such as SNAP, KAOS etc...) is a key problem given our ignorance of the physics underlying cosmic acceleration and the plethora of surveys planned. We propose a Bayesian design framework which (1) maximises the discrimination power of a survey without assuming any underlying dark energy model, (2) finds the best niche survey geometry given current data and future competing experiments, (3) maximises the cross-section for serendipitous discoveries and (4) can be adapted to answer specific questions (such as `is dark energy dynamical?'). Integrated Parameter Space Optimisation (IPSO) is a design framework that integrates projected parameter errors over an entire dark energy parameter space and then extremises a figure of merit (such as Shannon entropy gain wich we show is stable to off-diagonal covariance matrix perturbations) as a function of survey parameters using analytical, grid or MCMC techniques. IPSO is thus a flexible, model-independent and scalable framework that allows us to appropriately use prior information to design the best possible surveys.Keywords
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